class_docstr: Manages label issues in a Datalab.
  
  Parameters
  ----------
  datalab :
      A Datalab instance.
  
  k :
      The number of nearest neighbors to consider when computing pred_probs from features.
      Only applicable if features are provided and pred_probs are not.
  
  clean_learning_kwargs :
      Keyword arguments to pass to the :py:meth:`CleanLearning <cleanlab.classification.CleanLearning>` constructor.
  
  health_summary_parameters :
      Keyword arguments to pass to the :py:meth:`health_summary <cleanlab.dataset.health_summary>` function.
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docstr: Collects a set of input parameters for the health summary function based on
  any info available in the datalab.
  
  Parameters
  ----------
  pred_probs :
      The predicted probabilities for each example.
  
  kwargs :
      Keyword arguments to pass to the health summary function.
  
  Returns
  -------
  summary_parameters :
      A dictionary of parameters to pass to the health summary function.
docstr_tok: ["Collects","a","set","of","input","parameters","for","the","health","summary","function","based","on","any","info","available","in","the","datalab",".","Parameters","--","--","--","--","--","pred_probs",":","The","predicted","probabilities","for","each","example",".","kwargs",":","Keyword","arguments","to","pass","to","the","health","summary","function",".","Returns","--","--","--","-","summary_parameters",":","A","dictionary","of","parameters","to","pass","to","the","health","summary","function","."]
code: def _get_summary_parameters(self, pred_probs) -> Dict["str", Any]:
      """Collects a set of input parameters for the health summary function based on
      any info available in the datalab.
  
      Parameters
      ----------
      pred_probs :
          The predicted probabilities for each example.
  
      kwargs :
          Keyword arguments to pass to the health summary function.
  
      Returns
      -------
      summary_parameters :
          A dictionary of parameters to pass to the health summary function.
      """
      if "confident_joint" in self.health_summary_parameters:
          summary_parameters = {
              "confident_joint": self.health_summary_parameters[
                  "confident_joint"
              ]
          }
      elif all(
          [
              x in self.health_summary_parameters
              for x in ["joint", "num_examples"]
          ]
      ):
          summary_parameters = {
              k: self.health_summary_parameters[k]
              for k in ["joint", "num_examples"]
          }
      else:
          summary_parameters = {
              "pred_probs": pred_probs,
              "labels": self.datalab.labels,
          }
  
      summary_parameters[
          "class_names"
      ] = self.health_summary_parameters["class_names"]
  
      for k in ["asymmetric", "verbose"]:
          # Start with the health_summary_parameters, then override with kwargs
          if k in self.health_summary_parameters:
              summary_parameters[k] = self.health_summary_parameters[k]
  
      return summary_parameters  # will be called in `dataset.health_summary(**summary_parameters)`
  
file_path: cleanlab/cleanlab/datalab/internal/issue_manager/label.py
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional
  from sklearn.neighbors import KNeighborsClassifier
  from sklearn.preprocessing import OneHotEncoder
  import numpy
  from cleanlab.classification import CleanLearning
  from cleanlab.datalab.internal.issue_manager import IssueManager
  from cleanlab.internal.validation import assert_valid_inputs
label_desc: Use image node_id 6 for calling the LabelIssueManager obj's underlying member method code with example usage: obj._get_summary_parameters(pred_probs) and returns: summary_parameters